DreaMoving - Alibaba's open-source human video generation framework based on a diffusion model.

What is DreaMoving?

DreaMoving is a human video generation framework based on a diffusion model, developed by the research team at Alibaba Group. DreaMoving achieves precise control over human movements and appearance through a video control network and content guide, allowing users to generate personalized video content using simple text descriptions or image cues. The framework aims to generate high-quality, customized human videos, particularly capable of generating video content showing a target moving or dancing based on a given target identity and pose sequence.

DreaMoving official website entrance

• Official project homepage:https://dreamoving.github.io/dreamoving/
• arXiv research paper:https://arxiv.org/abs/2312.05107
• GitHub address:https://github.com/dreamoving/dreamoving-project
• ModelScope Community:https://www.modelscope.cn/studios/vigen/video_generation/summary
• Hugging Face Demo:https://huggingface.co/spaces/jiayong/Dreamoving

Features of DreaMoving

• Customized video generationDreaming can generate videos of people moving or dancing based on the target identity and posture sequence provided by the user, meeting the needs of personalized video content production.
• High controllabilityThrough Video ControlNet, Dreaming can precisely control the movement details of people in a video, ensuring that the generated video has a high degree of temporal consistency and motion realism.
• Identity preservationBy using a Content Guider, the framework can preserve the identity features of people in the video, such as their faces and clothing, ensuring that the generated video closely matches the target identity.
• Diverse input methodsUsers can be guided through video generation using text prompts, image prompts, or a combination of both, providing a flexible creative approach.
• Easy to use and adaptDreaMoving features a simple design, is easy to use, and can be adapted to various stylized diffusion models to generate diverse video results.

DreaMoving's technical architecture

The architecture of DreaMoving is based on the Stable Diffusion model and consists of three core parts, each with different functions and tasks, working together to achieve high-quality human video generation.

1. Denoising U-NetAs the foundational network for DreaMoving, it is responsible for the video generation process. The denoising U-Net iteratively recovers clear video frames from noise. Motion blocks are inserted after each U-Net block to enhance the temporal consistency and motion realism of the video.
2. Video ControlNetThis network is specifically designed to control the movement of people in videos. As a plug-in module, it works in conjunction with the denoising U-Net to control the movement of people by processing pose or depth sequences. Video ControlNet enables the generated video to exhibit precise motion patterns based on the input control sequence.
3. Content GuiderThe purpose of a content guide is to preserve the identifying features of people in a video, such as their faces and clothing. It uses an image encoder to precisely guide the appearance of the people while combining text cues to generate background content. The content guide uses a cross-attention mechanism to combine input text cues and image features to generate video content with specific identifying characteristics.

These three networks work together to enable Dreaming to generate high-quality human videos that possess both personalized identity features and precise motion control. Furthermore, Dreaming's architecture is designed for ease of use and adaptation, allowing it to be combined with various stylized diffusion models to produce diverse video generation results.

Application scenarios of DreaMoving

• Film and television productionIn film and television production, Dreaming can be used to create complex action scenes, especially when specific character performances or dance moves are required. It can help production teams quickly generate high-quality video content with limited budgets and time.
• Game developmentGame designers can use DreamMoving to generate realistic character animations, providing players with a richer and more immersive gaming experience. This is especially important in role-playing or action games, as it can enhance the game's immersion.
• Personalized video creationContent creators can use Dreaming to create personalized videos, such as dance challenges, impersonations, or other creative videos for social media. Users can quickly generate personalized video content with simple text or image input.
• Advertising and MarketingMarketers can use Dreaming to create engaging advertising videos, using customized character actions and scenarios to attract target audiences and improve advertising effectiveness and brand awareness.